Decision Trees

We have described two type of ML algorithms:

• Linear: linear regression, GLM, LDA, QDA
• Smoothing: bin smoother, loess, kNN

The first is lacks flexibility

The second is does not do well with the curse of dimensionality.

or, if we want to include 10% of the data we need to increase the window size to \( \sqrt{.10} \):

To include 10%, each dimension needs to cover \( .10^{1/p} \)

We use Regression Trees for continuous outcome:

1. Partitions space into \( J \) non-overlapping regions, \( R_1, R_2, \ldots, R_J \).
2. For every observation that falls within region \( R_j \), predict response as mean of response for training observations in \( R_j \).

The important observation is that Regression Trees create partition recursively

A recursive algorithm would look like this:

1. Find predictor \( j \) and value \( s \) that minimize RSS:

\[ \sum_{i:\, x_i \in R_1(j,s))} (y_i - \hat{y}_{R_1})^2 + \sum_{i:\, x_i \in R_2(j,s))} (y_i - \hat{y}_{R_2})^2 \]

Where \( R_1 \) and \( R_2 \) result from splitting predictor \( j \) at \( s \):

\[ R_1(j,s) = \{X|X_j < s\} \mbox{ and } R_2(j,s) \{X|X_j \geq s\} \]

When do we stop partitioning? We stop when:

1. adding a partition does not reduce RSS or
2. when partition has too few training observations.

Even then, trees built with this stopping criterion tend to overfit training data.

To avoid this, a post-processing step called pruning is used to make the tree smaller.

tree_control <-  tree.control(
  nobs = nrow(polls_2008),
  mincut = 1,
  minsize = 2,
  mindev = 0.001)

fit <- tree(Y~X, data = polls_2008,
            control = tree_control)
cv_polls <- cv.tree(fit)

Score function to choose predictors and values to partitions:

• Gini Index: \( \sum_{k=1}^K \hat{p}_{mk}(1-\hat{p}_{mk}) \), or
• Entropy: \( -\sum_{k=1}^K \hat{p}_{mk}\log(\hat{p}_{mk}) \)

where \( \hat{p}_{mk} \) is the proportion of training observations in partition \( m \) labeled as class \( k \). Both of these seek to partition observations into subsets that have the same labels.

We use income distribution as an example:

The population median is

m <- median(income)
m

[1] 45369.52

set.seed(1)
N <- 250
X <- sample(income, N)
M <- median(X)
M

[1] 44334.42

Idea behind bootstrap is to use sample as estimate of population: sample from the sample.

B <- 10^5
M_stars <- replicate(B, {
  X_star <- sample(X, N, replace = TRUE)
  M_star <- median(X_star)
})

True CI: 39765.15 51721.77

Bootstrap estimate: 39549.38 50278.73

This is much better than what we get if we mindlessly use the CLT:

[1] 27583.05 61085.80

If we know the distribution is normal, we can use the bootstrap to estimate the mean:

mean(Ms) + 1.96*sd(Ms)*c(-1,1)

[1] 38346.74 52635.97

mean(M_stars) + 1.96*sd(M_stars)*c(-1,1)

[1] 37741.55 51923.76

General idea: average multiple decision trees. We construct a forest with randomness.

The first idea is Bagging (bootstrap aggregation)

1. Build many decision trees \( T_1, T_2, \ldots, T_B \) from training set
2. Given a new observation, let each \( T_j \) predict \( \hat{y}_j \)
3. For regression: predict average \( \frac{1}{B} \sum_{j=1}^B \hat{y}_j \), for classification: predict with majority vote (most frequent class)

To create \( T_j, \, j=1,\ldots,B \) from training set of size \( N \):

1. create a bootstrap training set by sampling \( N \) observations from training set with replacement

2. build a decision tree from bootstrap training set

-> <-

The second Random Forests feature is to use a random selection of features to split when deciding partitions.

When building each tree \( T_j \), at each recursive partition only consider a randomly selected subset of predictors to check for best split.

This reduces correlation between trees in forest, improving prediction accuracy.

CART:  0.8055037

First RF:  0.801306

Second RF:  0.8264925

pred <- predict(fit, newdata = test_set, type = "class")
confusionMatrix(table(pred = pred, true = test_set$label))$overall[1]

 Accuracy 
0.9659199 

feature	Gini
pixel378	315.1068
pixel437	308.2002
pixel434	291.9889
pixel405	284.2842
pixel290	252.8761
pixel409	247.7304
pixel350	245.8441
pixel377	228.2036
pixel515	227.6764
pixel542	226.3225